17 citations · 34 across the 20 of their papers we have counts for
13 papers · 1 filter
Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures
Yujian Liu, Li An, Zelin He +5
Autonomous agents can increasingly propose ideas, modify code, and run experiments, creating an opportunity to accelerate scientific discovery using teams of collaborative AI resea…
Practical Online KV Cache Compaction for LLM Agents: An Empirical Study
Yujian Liu, Jiabao Ji, Li An +4
LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV cache compaction can reduce t…
VISUALSKILL: Multimodal Skills for Computer-Use Agents
Ziyan Jiang, Li An, Yujian Liu +5
Computer-use agents (CUAs) approach human-level performance on standardised benchmarks but still struggle on long-horizon tasks and unseen software. Existing skill libraries addres…
How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings
Yujian Liu, Jiabao Ji, Li An +3
Agent skills, which are reusable, domain-specific knowledge artifacts, have become a popular mechanism for extending LLM-based agents, yet formally benchmarking skill usage perform…
DEEPAMBIGQA: Ambiguous Multi-hop Questions for Benchmarking LLM Answer Completeness
Jiabao Ji, Min Li, Priyanshu Kumar +2
Large language models (LLMs) with integrated search tools show strong promise in open-domain question answering (QA), yet they often struggle to produce complete answer set to comp…
HardTests: Synthesizing High-Quality Test Cases for LLM Coding
Zhongmou He, Yee Man Choi, Kexun Zhang +6
Verifiers play a crucial role in large language model (LLM) reasoning, needed by post-training techniques such as reinforcement learning. However, reliable verifiers are hard to ge…
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
Bairu Hou, Yang Zhang, Jiabao Ji +4
We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs, which has been found to often produce inefficient and redundant thinkin…
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
Jiabao Ji, Yujian Liu, Yang Zhang +4
As Large Language Models (LLMs) demonstrate extensive capability in learning from documents, LLM unlearning becomes an increasingly important research area to address concerns of L…
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Jiabao Ji, Bairu Hou, Zhen Zhang +7
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerab…
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Jiabao Ji, Bairu Hou, Alexander Robey +5
Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content. While…
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Yujian Liu, Jiabao Ji, Tong Yu +6
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. However, the given table often does not contain sufficie…
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
Zhi Chen, Jijia Bao, Lu Chen +10
Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this…
WebSRC: A Dataset for Web-Based Structural Reading Comprehension
Xingyu Chen, Zihan Zhao, Lu Chen +5
Web search is an essential way for humans to obtain information, but it's still a great challenge for machines to understand the contents of web pages. In this paper, we introduce…